Comparative analysis of the Kluc tumor scRNAseq data

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Last updated: 2024-02-27

Checks: 5 2

Knit directory: PD1_mm/

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---

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absolute	relative
/home/hnatri/PD1_mm/	.
/home/hnatri/PD1_mm/code/utilities.R	code/utilities.R
/home/hnatri/PD1_mm/code/PD1_mm_themes.R	code/PD1_mm_themes.R
/home/hnatri/PD1_mm/code/CART_plot_functions.R	code/CART_plot_functions.R

Repository version: 18c1bd4

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Ignored files:
    Ignored:    analysis/figure/

Untracked files:
    Untracked:  Layer1_DoHeatmap.pdf
    Untracked:  Layer1_UMAP.pdf
    Untracked:  Layer1_UMAP_DotPlot.pdf
    Untracked:  Layer1_scImmuCC_label.csv
    Untracked:  Layer1_tSNE.pdf
    Untracked:  Layer2_Bcell_DoHeatmap.pdf
    Untracked:  Layer2_Bcell_UMAP.pdf
    Untracked:  Layer2_Bcell_UMAP_DotPlot.pdf
    Untracked:  Layer2_Bcell_scImmuCC_label.csv
    Untracked:  Layer2_Bcell_tSNE.pdf
    Untracked:  Layer2_DC_DoHeatmap.pdf
    Untracked:  Layer2_DC_UMAP.pdf
    Untracked:  Layer2_DC_UMAP_DotPlot.pdf
    Untracked:  Layer2_DC_scImmuCC_label.csv
    Untracked:  Layer2_DC_tSNE.pdf
    Untracked:  Layer2_ILC_scImmuCC_label.csv
    Untracked:  Layer2_Macrophage_DoHeatmap.pdf
    Untracked:  Layer2_Macrophage_UMAP.pdf
    Untracked:  Layer2_Macrophage_UMAP_DotPlot.pdf
    Untracked:  Layer2_Macrophage_scImmuCC_label.csv
    Untracked:  Layer2_Macrophage_tSNE.pdf
    Untracked:  Layer2_Monocyte_DoHeatmap.pdf
    Untracked:  Layer2_Monocyte_UMAP.pdf
    Untracked:  Layer2_Monocyte_UMAP_DotPlot.pdf
    Untracked:  Layer2_Monocyte_scImmuCC_label.csv
    Untracked:  Layer2_Monocyte_tSNE.pdf
    Untracked:  Layer2_NK_DoHeatmap.pdf
    Untracked:  Layer2_NK_UMAP.pdf
    Untracked:  Layer2_NK_UMAP_DotPlot.pdf
    Untracked:  Layer2_NK_scImmuCC_label.csv
    Untracked:  Layer2_NK_tSNE.pdf
    Untracked:  Layer2_Tcell_DoHeatmap.pdf
    Untracked:  Layer2_Tcell_UMAP.pdf
    Untracked:  Layer2_Tcell_UMAP_DotPlot.pdf
    Untracked:  Layer2_Tcell_scImmuCC_label.csv
    Untracked:  Layer2_Tcell_tSNE.pdf
    Untracked:  Layer3_CD4_DoHeatmap.pdf
    Untracked:  Layer3_CD4_UMAP.pdf
    Untracked:  Layer3_CD4_UMAP_DotPlot.pdf
    Untracked:  Layer3_CD4_scImmuCC_label.csv
    Untracked:  Layer3_CD4_tSNE.pdf
    Untracked:  Layer3_CD8_DoHeatmap.pdf
    Untracked:  Layer3_CD8_UMAP.pdf
    Untracked:  Layer3_CD8_UMAP_DotPlot.pdf
    Untracked:  Layer3_CD8_scImmuCC_label.csv
    Untracked:  Layer3_CD8_tSNE.pdf

Unstaged changes:
    Modified:   analysis/annotate.Rmd
    Modified:   analysis/comparative_analysis.Rmd
    Modified:   analysis/processing.Rmd
    Modified:   cluster_markers.tsv
    Modified:   code/utilities.R

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/comparative_analysis.Rmd) and HTML (docs/comparative_analysis.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	18c1bd4	heinin	2024-02-27	Cell type proportion testing
html	18c1bd4	heinin	2024-02-27	Cell type proportion testing
Rmd	3e207b9	heinin	2024-02-26	Added scImmuCC annotations
Rmd	196db6a	heinin	2024-02-26	Starting the comparative analysis

---

Introduction

Adding annotations and comparing treatment groups/timepoints.

Packages and environment variables

suppressPackageStartupMessages({
  #library(cli)
  library(Seurat)
  library(SeuratObject)
  library(SeuratDisk)
  library(tidyverse)
  library(tibble)
  library(ggplot2)
  library(ggpubr)
  library(ggrepel)
  library(workflowr)
  library(googlesheets4)
  library(scProportionTest)})

setwd("/home/hnatri/PD1_mm/")
set.seed(9999)
options(ggrepel.max.overlaps = Inf)

# Colors, themes, cell type markers, and plot functions
source("/home/hnatri/PD1_mm/code/utilities.R")
source("/home/hnatri/PD1_mm/code/PD1_mm_themes.R")
source("/home/hnatri/PD1_mm/code/CART_plot_functions.R")

Importing data

seurat_data <- readRDS("/tgen_labs/banovich/BCTCSF/PD1_mm_Seurat/PD1_mm_Seurat_merged.Rds")

DimPlot of cell type annotations

celltypes <- sort(as.character(unique(seurat_data$celltype)))
plot_colors <- colorRampPalette(brewer.pal(11, "Paired"))(length(celltypes))
names(plot_colors) <- celltypes

DimPlot(seurat_data,
        group.by = "celltype",
        reduction = "umap",
        raster = T,
        label = T,
        cols = plot_colors) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend()

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
18c1bd4	heinin	2024-02-27

DimPlot(seurat_data,
        group.by = "celltype",
        split.by = "Treatment",
        reduction = "umap",
        raster = T,
        #label = T,
        cols = plot_colors) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend()

Cell type proportion differences

In scatter plots, the proportions of cell types in each pair of treatment groups are plotted against each other with one group on each axis. The forest plots show the significance level.

unique(seurat_data$Treatment)

[1] "ADJ"  "CTRL" "NEO" 

unique(seurat_data$Day)

[1] 16 12

table(seurat_data$celltype, seurat_data$Treatment)

       
         ADJ CTRL  NEO
  M1    1090 2163 1603
  Mono1  436 1444 2735
  M2     910 1634 1452
  L1     571 1420 1475
  Neut1  526 1498 1321
  L2    1117 1045 1063
  NK     579  992 1167
  M3     449 1117 1054
  M4     502  921 1107
  M5     252  665 1454
  NOS1   251 1619  498
  DC     379  773  772
  M6     132  763  590
  B1     311  568  553
  Treg   178  521  523
  M7     160  403  615
  B2     171  320  347
  L3     269  305  225
  L4      50  154  371
  M8      20  281  185

create_clusterpropplot(seurat_data,
                       group_var = "Treatment",
                       group1 = "ADJ",
                       group2 = "CTRL",
                       plot_var = "celltype",
                       plot_colors = plot_colors,
                       var_names = c("ADJ", "CTRL"),
                       legend_title = "Treatment")

Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

Past versions of unnamed-chunk-4-1.png

Version	Author	Date
18c1bd4	heinin	2024-02-27

create_clusterpropplot(seurat_data,
                       group_var = "Treatment",
                       group1 = "NEO",
                       group2 = "CTRL",
                       plot_var = "celltype",
                       plot_colors = plot_colors,
                       var_names = c("NEO", "CTRL"),
                       legend_title = "Treatment")

Past versions of unnamed-chunk-4-2.png

Version	Author	Date
18c1bd4	heinin	2024-02-27

create_clusterpropplot(seurat_data,
                       group_var = "Treatment",
                       group1 = "ADJ",
                       group2 = "NEO",
                       plot_var = "celltype",
                       plot_colors = plot_colors,
                       var_names = c("ADJ", "NEO"),
                       legend_title = "Treatment")

Past versions of unnamed-chunk-4-3.png

Version	Author	Date
18c1bd4	heinin	2024-02-27

# Day 12
create_clusterpropplot(subset(seurat_data, subset = Day == 12),
                       group_var = "Treatment",
                       group1 = "NEO",
                       group2 = "CTRL",
                       plot_var = "celltype",
                       plot_colors = plot_colors,
                       var_names = c("NEO", "CTRL"),
                       legend_title = "Treatment, Day 12")

Past versions of unnamed-chunk-4-4.png

Version	Author	Date
18c1bd4	heinin	2024-02-27

# Day 16
create_clusterpropplot(subset(seurat_data, subset = Day == 16),
                       group_var = "Treatment",
                       group1 = "ADJ",
                       group2 = "CTRL",
                       plot_var = "celltype",
                       plot_colors = plot_colors,
                       var_names = c("ADJ", "CTRL"),
                       legend_title = "Treatment, Day 16")

Past versions of unnamed-chunk-4-5.png

Version	Author	Date
18c1bd4	heinin	2024-02-27

create_clusterpropplot(subset(seurat_data, subset = Day == 16),
                       group_var = "Treatment",
                       group1 = "NEO",
                       group2 = "CTRL",
                       plot_var = "celltype",
                       plot_colors = plot_colors,
                       var_names = c("NEO", "CTRL"),
                       legend_title = "Treatment, Day 16")

Past versions of unnamed-chunk-4-6.png

Version	Author	Date
18c1bd4	heinin	2024-02-27

create_clusterpropplot(subset(seurat_data, subset = Day == 16),
                       group_var = "Treatment",
                       group1 = "ADJ",
                       group2 = "NEO",
                       plot_var = "celltype",
                       plot_colors = plot_colors,
                       var_names = c("ADJ", "NEO"),
                       legend_title = "Treatment, Day 16")

Past versions of unnamed-chunk-4-7.png

Version	Author	Date
18c1bd4	heinin	2024-02-27

# Using scProportionTest
prop_test <- sc_utils(seurat_data)

prop_test <- permutation_test(
  prop_test, cluster_identity = "celltype",
  sample_1 = "CTRL", sample_2 = "ADJ",
  sample_identity = "Treatment")

perm_plot <- permutation_plot(prop_test)

perm_plot + scale_colour_manual(values = c("tomato", "azure2")) +
  #NoLegend() +
  ggtitle("ADJ vs. CTRL, all timepoints")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Past versions of unnamed-chunk-4-8.png

Version	Author	Date
18c1bd4	heinin	2024-02-27

prop_test <- permutation_test(
  prop_test, cluster_identity = "celltype",
  sample_1 = "CTRL", sample_2 = "NEO",
  sample_identity = "Treatment")

perm_plot <- permutation_plot(prop_test)

perm_plot + scale_colour_manual(values = c("tomato", "azure2")) +
  #NoLegend() +
  ggtitle("NEO vs. CTRL, all timepoints")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Past versions of unnamed-chunk-4-9.png

Version	Author	Date
18c1bd4	heinin	2024-02-27

prop_test <- permutation_test(
  prop_test, cluster_identity = "celltype",
  sample_1 = "ADJ", sample_2 = "NEO",
  sample_identity = "Treatment")

perm_plot <- permutation_plot(prop_test)

perm_plot + scale_colour_manual(values = c("tomato", "azure2")) +
  #NoLegend() +
  ggtitle("NEO vs. ADJ, all timepoints")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Past versions of unnamed-chunk-4-10.png

Version	Author	Date
18c1bd4	heinin	2024-02-27

# Day 12 only
# For day 12, no ADJ sample
prop_test <- sc_utils(subset(seurat_data, subset = Day == 12))

prop_test <- permutation_test(
  prop_test, cluster_identity = "celltype",
  sample_1 = "CTRL", sample_2 = "NEO",
  sample_identity = "Treatment")

perm_plot <- permutation_plot(prop_test)

perm_plot + scale_colour_manual(values = c("tomato", "azure2")) +
  #NoLegend() +
  ggtitle("NEO vs. CTRL, day 12")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Past versions of unnamed-chunk-4-11.png

Version	Author	Date
18c1bd4	heinin	2024-02-27

# Day 16 only
prop_test <- sc_utils(subset(seurat_data, subset = Day == 16))

prop_test <- permutation_test(
  prop_test, cluster_identity = "celltype",
  sample_1 = "CTRL", sample_2 = "ADJ",
  sample_identity = "Treatment")

perm_plot <- permutation_plot(prop_test)

perm_plot + scale_colour_manual(values = c("tomato", "azure2")) +
  #NoLegend() +
  ggtitle("ADJ vs. CTRL, day 16")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Past versions of unnamed-chunk-4-12.png

Version	Author	Date
18c1bd4	heinin	2024-02-27

prop_test <- permutation_test(
  prop_test, cluster_identity = "celltype",
  sample_1 = "CTRL", sample_2 = "NEO",
  sample_identity = "Treatment")

perm_plot <- permutation_plot(prop_test)

perm_plot + scale_colour_manual(values = c("tomato", "azure2")) +
  #NoLegend() +
  ggtitle("NEO vs. CTRL, day 16")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Past versions of unnamed-chunk-4-13.png

Version	Author	Date
18c1bd4	heinin	2024-02-27

prop_test <- permutation_test(
  prop_test, cluster_identity = "celltype",
  sample_1 = "ADJ", sample_2 = "NEO",
  sample_identity = "Treatment")

perm_plot <- permutation_plot(prop_test)

perm_plot + scale_colour_manual(values = c("tomato", "azure2")) +
  #NoLegend() +
  ggtitle("NEO vs. ADJ, day 16")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Past versions of unnamed-chunk-4-14.png

Version	Author	Date
18c1bd4	heinin	2024-02-27

Session information

sessionInfo()

R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ComplexHeatmap_2.18.0       viridis_0.6.3              
 [3] viridisLite_0.4.2           circlize_0.4.15            
 [5] plyr_1.8.8                  RColorBrewer_1.1-3         
 [7] scProportionTest_0.0.0.9000 googlesheets4_1.1.0        
 [9] workflowr_1.7.1             ggrepel_0.9.3              
[11] ggpubr_0.6.0                lubridate_1.9.2            
[13] forcats_1.0.0               stringr_1.5.0              
[15] dplyr_1.1.2                 purrr_1.0.1                
[17] readr_2.1.4                 tidyr_1.3.0                
[19] tibble_3.2.1                ggplot2_3.4.2              
[21] tidyverse_2.0.0             SeuratDisk_0.0.0.9021      
[23] Seurat_5.0.1                SeuratObject_5.0.1         
[25] sp_1.6-1                   

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.20       splines_4.3.0          later_1.3.1           
  [4] cellranger_1.1.0       polyclip_1.10-4        fastDummies_1.7.3     
  [7] lifecycle_1.0.3        rstatix_0.7.2          doParallel_1.0.17     
 [10] rprojroot_2.0.3        globals_0.16.2         processx_3.8.1        
 [13] lattice_0.21-8         hdf5r_1.3.8            MASS_7.3-60           
 [16] backports_1.4.1        magrittr_2.0.3         plotly_4.10.2         
 [19] sass_0.4.6             rmarkdown_2.22         jquerylib_0.1.4       
 [22] yaml_2.3.7             httpuv_1.6.11          sctransform_0.4.1     
 [25] spam_2.9-1             spatstat.sparse_3.0-1  reticulate_1.29       
 [28] cowplot_1.1.1          pbapply_1.7-0          abind_1.4-5           
 [31] Rtsne_0.16             BiocGenerics_0.48.1    git2r_0.32.0          
 [34] S4Vectors_0.40.2       IRanges_2.36.0         irlba_2.3.5.1         
 [37] listenv_0.9.0          spatstat.utils_3.0-3   goftest_1.2-3         
 [40] RSpectra_0.16-1        spatstat.random_3.1-5  fitdistrplus_1.1-11   
 [43] parallelly_1.36.0      leiden_0.4.3           codetools_0.2-19      
 [46] tidyselect_1.2.0       shape_1.4.6            farver_2.1.1          
 [49] stats4_4.3.0           matrixStats_1.0.0      spatstat.explore_3.2-1
 [52] googledrive_2.1.0      jsonlite_1.8.5         GetoptLong_1.0.5      
 [55] ellipsis_0.3.2         progressr_0.13.0       iterators_1.0.14      
 [58] ggridges_0.5.4         survival_3.5-5         foreach_1.5.2         
 [61] tools_4.3.0            ica_1.0-3              Rcpp_1.0.10           
 [64] glue_1.6.2             gridExtra_2.3          xfun_0.39             
 [67] withr_2.5.0            fastmap_1.1.1          fansi_1.0.4           
 [70] callr_3.7.3            digest_0.6.31          timechange_0.2.0      
 [73] R6_2.5.1               mime_0.12              colorspace_2.1-0      
 [76] scattermore_1.2        tensor_1.5             spatstat.data_3.0-1   
 [79] utf8_1.2.3             generics_0.1.3         data.table_1.14.8     
 [82] httr_1.4.6             htmlwidgets_1.6.2      whisker_0.4.1         
 [85] uwot_0.1.14            pkgconfig_2.0.3        gtable_0.3.3          
 [88] lmtest_0.9-40          htmltools_0.5.5        carData_3.0-5         
 [91] dotCall64_1.0-2        clue_0.3-64            scales_1.2.1          
 [94] png_0.1-8              knitr_1.43             rstudioapi_0.14       
 [97] rjson_0.2.21           tzdb_0.4.0             reshape2_1.4.4        
[100] nlme_3.1-162           cachem_1.0.8           zoo_1.8-12            
[103] GlobalOptions_0.1.2    KernSmooth_2.23-21     parallel_4.3.0        
[106] miniUI_0.1.1.1         pillar_1.9.0           vctrs_0.6.2           
[109] RANN_2.6.1             promises_1.2.0.1       car_3.1-2             
[112] xtable_1.8-4           cluster_2.1.4          evaluate_0.21         
[115] cli_3.6.1              compiler_4.3.0         rlang_1.1.1           
[118] crayon_1.5.2           future.apply_1.11.0    ggsignif_0.6.4        
[121] labeling_0.4.2         ps_1.7.5               getPass_0.2-4         
[124] fs_1.6.2               stringi_1.7.12         deldir_1.0-9          
[127] munsell_0.5.0          lazyeval_0.2.2         spatstat.geom_3.2-1   
[130] Matrix_1.6-5           RcppHNSW_0.5.0         hms_1.1.3             
[133] patchwork_1.1.2        bit64_4.0.5            future_1.32.0         
[136] shiny_1.7.4            highr_0.10             ROCR_1.0-11           
[139] gargle_1.4.0           igraph_1.4.3           broom_1.0.4           
[142] bslib_0.4.2            bit_4.0.5